Vitreos Publication

White Paper | Population Health Management: Real-Time State-of-Health Analysis

Leveraging EHR Data to design and execute provider-driven care management programs.

The Affordable Care Act opens the door to a wealth of opportunity for hospitals and physician groups. They are beginning to adapt to the new pay-for performance and bundled payment systems, and develop population-based care management programs. While the goal of this piece of legislation is to hold hospitals and physicians jointly responsible for quality and cost of care, the new payment models span the entire care continuum including primary care physicians, specialists, hospitals, and other medical professionals. The biggest winners will be the ones who can improve quality of care while driving down costs. Those that focus first on preventive care for top chronic illnesses will be the first to reach the finish line.

Innovative healthcare providers take the lead by developing coordinated care systems that embody the core principles of preventive care: Patient-Centric Medical Homes (PCMHs) and Accountable Care Organizations (ACOs). Physician networks are adopting the PCMH model, relying on primary care physicians (PCPs) and care coordinators as the central hub for care, and looking to specialists when necessary. Medical homes deliver preventive care to the entire spectrum of patients, from healthy to chronic, with the goal of avoiding admissions to acute care facilities.

CDC on Chronic Diseases

7 out of 10 deaths among Americans each year are from chronic diseases. Heart disease, cancer and stroke account for more than 50% of all deaths each year.
In 2005, 133 million Americans – almost 1 out ofevery 2 adults – had at least one chronic illness.
Obesity has become a major health concern. 1 in every 3 adults is obese and almost 1 in 5 youth between the ages of 6 and 19 is obese (BMI ≥ 95th percentile of the CDC growth chart).
Diabetes continues to be the leading cause of kidney failure, non-traumatic lower-extremity amputations, and blindness among adults, aged 20-74.


Chronic diseases account for the majority of acute care costs (in-patient, out-patient, and ER). Controlling acute care admissions for chronic disease is essential to control healthcare costs. Therefore, the effort to minimize healthcare costs must begin with managing top chronic conditions. According to the CDC, "Chronic diseases are the leading cause of death and disability in the US,"1 and Healthcare Cost Monitor underscores this fact revealing that, "Seventy-six percent of Medicare spending is on patients with five or more chronic diseases."2 The Agency of Healthcare Research and Quality also emphasizes the high cost of chronic conditions.3

1 "Chronic disease and health promotion." Centers for Disease Control and Prevention. Center for Disease Control, 2010. Web. 26 Feb 2012.

Regardless of which payment model becomes predominant (shared savings, bundled payment, or ACO), in order to bend the healthcare cost curve, healthcare providers must focus on preventive care for chronic care patients. Provider organizations will need an innovative approach to redesign care processes, with a focus on keeping chronic care patients healthy and out of ERs and hospitals.

AHRQ on Cost of Chronic Conditions

The 15 most expensive health conditions account for 44 percent of total healthcare expenses.
Patients with multiple chronic conditions cost up to seven times as much as patients with only one chronic condition.


For years, healthcare insurance companies (payers) have mined claims data for chronic patients and have built predictive models to identify high-risk patients. Armed with historical reports, case managers designed intervention programs that were meant to prevent complications among chronic patients and reduce ER visits and hospitalizations.

While this approach has seen some success, limitations far outweigh merits. Data used by payers to flag high risk patients is historical claims data — primarily costs, admissions, and diagnoses. Because this view is retrospective and heavily biased toward cost, patients with past high acute care costs are flagged as “risky”, regardless of their current state-of-health. Furthermore, regression and time series risk models are typically updated only annually.

Healthcare Cost Monitor on Chronic Disease Spending

Seventy-six percent of Medicare spending is on patients with five or more chronic diseases.
Currently 10% of healthcare dollars are spent on overall direct costs related to diabetes, amounting to $92 billion a year (1.5 times the amount spent on stroke or heart disease). The Centers for Disease Control and Prevention predicts that spending on diabetes care will reach $192 billion in 2020.
According to the Milken institute, overall cost of heart disease is predicted to reach $186 billion in 2023. diseases

Most physicians are highly skeptical of claimsbased predictive models because they have no clinical basis, and give no consideration to an individual's current state-of-health. Moreover, there is a complete lack of causation, "Why is a patient considered high-risk? What are the clinical reasons for the score? How do we lower the patient's risk score? How does the score measure the effectiveness of my care management program?"

2 Swartz, Kimberly. "Projected Cost of Chronic Diseases." healthcare Cost Monitor. healthcare Cost Monitor, n.d. Web. 26 Feb 2012. diseases/.

3 Stanton, M. W.. "The High Concentration of US HealthCare Expenditures." Agency for Healthcare Research and Quality. AHRQ, 2006. Web. 26 Feb 2012.

These models lack a correlation to clinical information. For example, a physician will acknowledge a high risk score if there is evidence that the patient has a high BMI, and the HbA1C has been consistently high over the past year and is trending higher. The score becomes even more credible when there is evidence of ER admissions or acute care inpatient admissions.

Unfortunately, an individual’s current state-of-health has no bearing on his or her claims-based risk score. Claims-based risk scores are created with regression analysis at a population level to predict scores at the patient level. Individual scores are relative to the population, therefore could change as the population changes, even with no change in the individual’s state-of-health.

Not only are today’s calculations unsuitable for determining a patient’s true risk, they provide no insight on how an individual’s score improves or deteriorates after each clinical visit. Information lags so far behind; physicians are given no insight to actively manage ongoing care. Claims-based risk scores are not actionable – they provide no insight for care at the provider level.

Claims-based risk scores are also deficient because they do not adequately represent the population. Reports provided by payers are used primarily by case managers, who in most cases work for a payer. Physicians reject these reports as a basis for their own effectiveness in managing patients, because they are only a subset of their total population. Furthermore, payer reports are not typically useful for evidence-based care, to identify and implement clinical best practices. Finally, they are inadequate for measuring physician performance to design incentive programs.

In order to use payer reports across an entire population, a provider would first need to normalize multiple payers’ risk scoring systems, then aggregate the information. Because each payer has a unique methodology, there is little chance that the resulting information would be accurate or meaningful for developing care management programs.

Considering today’s approach to developing care management programs and understanding physician effectiveness, it’s important to remember that CMS does not provide patient risk scores. The fact that Medicare patients account for the majority of chronic patients and populations, other payers’ risk reports incorporate only a small fraction all chronic patients. Therefore, the impact of using individual (or even combined) claims-based payer risk reports is minimal in any effort to bend the overall patient population healthcare cost curve at the provider level.

Current thinking and efforts create a disproportionate focus on existing chronic patients. The intervention approach is designed specifically for this group, while wellness programs reflect only the hope that the healthy population will remain so. Because today’s healthy patients are largely ignored, yet will become tomorrow’s chronic patients, this approach is deeply flawed. If providers delay uncovering and examining causes until a chronic diagnosis emerges, there is no opportunity to avoid a chronic scenario. A better approach is to monitor all patients, healthy and chronic, for risk of hospitalizations. Unfortunately, current claims-based predictive risk models allow no room for this approach.

Claims-based risk models create a grave conflict for today’s physicians. To realize bonuses, they must choose cost of care over effective care. To make matters worse, incentives do not reward every healthcare professional that has an impact on patient health. Conversely, payers strive to minimize bonuses to physicians and networks. Physicians perceive that payers have an “upper hand” and can deny bonuses as models change, and assert that costs were higher than “reasonable” against the statistical model. As a result, there is inherent conflict between physicians and payers.

Progressive medical groups do not use claims-based patient risk reports created by payers to develop care management programs. And, until today, there has been a stark absence of credible decision support hindering proactive care management. As a result, health professionals have not had the ability to focus on population state-of-health as a means to reduce ER and hospital admissions.


Today, most large physician groups and medical homes already use at least a basic EHR system. CMS predicts that by 2014, more than fifty percent of all eligible medical professionals in the U.S. will use EHR. According to Frost & Sullivan, the ambulatory EHR market will explode to $3 billion by 2013. This is a transformational shift, because for the first time in history, clinical information is digitally available in real time, with reasonable availability of laboratory results and patient vital data.

Today, most large physician groups and medical homes already use at least a basic EHR system. CMS predicts that by 2014, more than fifty percent of all eligible medical professionals in the U.S. will use EHR. According to Frost & Sullivan, the ambulatory EHR market will explode to $3 billion by 2013. This is a transformational shift, because for the first time in history, clinical information is digitally available in real time, with reasonable availability of laboratory results and patient vital data.

These systems use patient medical records to measure state-of-health, and evaluate the effectiveness of care programs and evidence-based medicine. Real-time clinical data from EHR records is also being used to create sustainable, repeatable programs to reduce the number of high-risk patients, and design individualized care management programs. Using current clinical data for analysis rather than historical claim data means that healthcare providers create programs that are meaningful and effective for their specific population.

The new care management decision support systems use actual clinical data, and there is little or no analysis or interpretation required by the physician. As a result, a care coordinator can take ownership of care management, so that primary physicians can focus on delivering patient care. In light of predictions for the short supply of doctors over the next few years, this is good news for patients and providers alike.


Using real-time clinical data from EHR records, healthcare providers now have the capacity to design a closed-loop population care management program (Figure 1). A well-designed program delivers primary care to drive higher quality, reduce costs, and deliver greater value in healthcare.

The very foundation of the well-designed program is population state-of-health stratification, the ability to categorize patients into high (red), moderate (yellow), and low (green) risk groups by chronic condition.

Population stratification makes it possible to design customized programs for high-risk patients, execute and monitor programs, and measure the performance of clinical teams for incentive management.

Population state-of-health (SOH) Stratification

state-of-health stratification provides actionable and measurable information about actual health status at the population and patient levels, with visibility of controllable and non-controllable factors. An SOH model takes into consideration every patient’s age, gender, ethnicity, family history, all clinical factors (like BMI, lipid panel, blood HM, PFTs) and co-morbidities, and delivers an accurate SOH score for every encounter and for the entire population (score ranges 0 to 100). A low score indicates excellent health, and as the number increases so does the likelihood of complication(s) and hospitalization within 12 to 18 months. SOH is a “risk predictor”.

However, it is also an indicator of the quality of care delivered. If the score trends down, the quality of care is good, because health is improving. In this sense, the trend of the SOH score is a measure for quality of primary care.

While payers have their own calculation and definition for “risk”, the remainder of this article uses the terms “Risk” and “state-of-health” interchangeably.

Origins of state-of-health (SOH) Models

Nationally accepted clinical models are the basis for state-of-health models. In some cases, when the data did not contain all the parameters required to compute SOH scores, assumptions and approximations were considered and validated with physicians to ensure the integrity of the models. The SOH models were then validated against historical data.

SOH scores are calculated at the patient level and rolled up to a population level (Figure 2). In this example, each row corresponds to a physician's patient population. It shows the patient count, the number of office visits (encounter) and the average population SOH score for each chronic disease. “Red” signifies “high risk” scores. Physicians and care coordinators use the easy-todigest visual information to focus on high risk populations, and drill down to individual patients to understand factors that contribute to scores.


Figure 2 Population SOH (Risk) Stratification by Physician.
Focus on prevention and screening; monitor compliance for chronic conditions

This approach allows healthcare providers to design meaningful preventive care programs for the exact population, and create individualized programs for specific patients.

Chronic Disease Management
Patients who comply with prescribed care programs are typically more successful in managing chronic conditions. This is where care coordinators play an important role. Leveraging state-of-health scores, care coordinators pinpoint high risk patients by chronic condition, and best evidence guidelines become the basis for customized care management programs. The care management program is integrated with the care management execution system that includes patient scheduling, outbound call centers, home visits, patient portals and emails. While the disease management program identifies needs, the execution system promotes compliance with treatments, medications, scheduling laboratory tests, attending educational counseling sessions, and other prescribed activities.Monitoring gaps in care established by evidence-based care, patients’ SOH trends, and underlying clinical drivers over time, care coordinators can identify patients that need their attention.

Care Coordination
Physicians who improved the state-of-health for their population (i.e. lower the score) over a one to three year period established and used better clinical protocols (i.e. best practice care management programs). In one instance, one physician’s CHF population risk increased to 55%, while another’s dropped to 5% (Figure 3). Analyzing SOH population trend by physician population, the team of physicians identified the most effective clinical protocols for the patient population and standardized around best evidence care. The physician team also used SOH scores as a measure of quality of primary care, resource utilization, costs, and patient experience to establish best evidence care protocols, to lower cost and improve patient experience. (Figure 4).


Figure 3 - Effectiveness of two physician CHF populations.
Use best practices within the risk group for evidence-based care coordination: medicines, treatment levels,
frequency of visits; by risk group.


Figure 4 - Population Chart / Cost-Quality Grid, High-Medium-Low Risk
Population performance: Map patients on quality and total cost across the continuum-of-care
(ambulatory and acute). Identify optimal preventive care levels to minimize lifecycle cost over a time period by chronic condition.

Incentive management
It is not enough to simply design and launch new programs. If financial incentives for healthcare professionals are not aligned with performance, success may be temporary and hard to sustain. Effective incentive programs distinctly drive higher quality and reduce costs for greater value in healthcare:

Align team incentives with population quality and cost performance targets (physicians and care coordinators)
Establish and share best evidence practices by chronic condition
Encourage teamwork to lower healthcare costs
Illustrate accurate physician and clinical coordinator population performance, and the impact to incentives
Incentive programs reward care teams for reducing population risk scores, improving patient satisfaction scores, and reducing overall patient costs. Continuum of care dashboards (ambulatory and acute) are useful in designing incentive programs and illustrate risk-cost-quality details for each patient (Figure 5).


Figure 5 - Continuum of Care Analysis by Patient, Preventive Care Impact on Acute Care Costs
Monitor how much total inpatient and outpatient care (cost and quality) is being provided to the risk panel; identify patient outliers.

Patient SOH scores can be rolled up to population averages. For example, one incentive program dashboard maps physician/care coordinator teams on a cost-quality grid (Figure 6). In this case, the quality metric captures population SOH, ACO quality measures, and patient satisfaction scores. The intersection of the crosshairs is the target for quality and cost for the specific patient population. Each bubble corresponds to a specific physician- care coordinator team, and the size of the bubble illustrates the size of the population they manage. The distance of each bubble from the crosshair indicates the positive or negative variance from the target and is proportional to each team’s bonus or penalty.

Figure 6 – Physician value index used for incentive management for care teams.
Report shared savings by plan by physician on a periodic basis and show the impact of actions on their “pocketbook”.

Results | Validating the SOH Model APPROACH
Using SOH models as a surrogate for primary care quality and the indicator of possible hospitalization is a new concept and will become the contemporary paradigm for chronic disease management. Therefore, it was important to understand how effectively the model and scores could predict hospitalizations against historical patient population data. To validate the models, researchers compared the new SOH model against that of a leading claims-based risk model (the payer model).

The insurance payer used claims data (patient age, gender, ethnicity, previous ER, IP admissions, costs, diagnosis and other claims files data). They calculated a risk score, a number between 0 and 5000.

For the SOH model, researchers used real-time clinical data (patient age, gender, ethnicity, vital signs, lab results and treatment medications). The SOH model did not include past ER or IP admissions data. The SOH model established a risk score between 0 and 100 for diabetes (Table 1).

Total diabetes patients  
(type 1 and 2, complicated and uncomplicated) 737
Time period (2010) 1 year
IP admissions 53
ER visits 95
Table 1 - SOH Validation  

Next, researchers calculated a SOH score for each patient using historical data over two years (2008-2009), and stratified the population based on SOH scores. Researchers compared SOH scores to actual IP admissions and ER visits.

Inpatient Admissions

Figure 7 shows total hospitalized patients as a ratio of the total diabetic patients for that SOH band. For example, in the SOH band 50-60, 20% of all patients were hospitalized. As the score increased, the ratio of patients within that band also increased. At very high scores, all patients were hospitalized. Thus, Figure 7 validates the accuracy and predictive power of the SOH score.


Figure 7- Ratio of Hospitalized Patients to Total Diabetic Patients

Creating a SOH Composite
Next, researchers compared the SOH results to the payer’s claims-based actuarial model. Figure 8 shows the relationship between the payer risk scores and IP admissions. In the 250-500 risk band, the ratio of admitted patients is higher than the SOH model. Since most patients are in this band, the predictive power of the payer’s model at low risk scores is diminished. Similarly, at higher risk scores, the predictive power of the payer’s model is only 50% whereas the researchers’ SOH model is closer to 100% accurate.


Figure 8 - Relationship between the payer risk scores and IP admissions.

ER visits
Figure 9 shows a similar comparison, SOH bands and ER visits. This comparison and the uniform curve further substantiates the SOH model as a valid and accurate predictor of ER admissions. Similarly, Figure 10 shows the payer’s model for ER admissions, which is clearly weak at both low and high risk bands.

state-of-health models are highly accurate and predictive, and ideally suited for chronic care population management by chronic condition. Using SOH scores, care coordinators can correctly identify and focus on high risk patients with a great risk of hospitalization in the short term. Given the rapid adoption of EHRs among primary care physicians and groups, the data required to build SOH models is readily available now, and will continue to expand over the next two years.

Healthcare providers can enable continuous improvement using SOH models together with care management programs. This approach has already been institutionalized in a number of leading medical homes like Medical Clinic of North Texas (MCNT). Within these organizations, there are a wide variety of individuals who actively use these models in their daily work, and can be described as:

  • Administrators & Management, to quantify the effectiveness of care management programs, measure productivity, and monitor incentive programs.
  • Physicians, to define and/or leverage best practices in managing disease, in line with their desire for evidence-based care. By analyzing SOH scores and understanding drivers, they have more insight to deliver better care.
  • Care coordinators, who are primarily interested in identifying high risk patients, to understand risk factors, develop individual care programs, and monitor patient compliance.

Medical Clinic of North Texas (MCNT), a Level 3 Recognition by the National Committee for Quality Assurance (NCQA) Physician Practice Connections ® - Patient Centered Medical Home™ (PPCPCMH) has been a pioneer user of SOH based population management approach.

MCNT demonstrated a stellar FY 2010 performance with Total Medical Cost trend for their managed population of 2.4% better than market, is a culmination of various quality of care drivers:

  • Potential avoidable ER visits decreased by 13.3%
  • OP Diagnostics trended only 1.9% vs. market trend of 9.7%
  • OP Surgical trended 5.6% vs. market trend of 15%
  • Utilization of CCD Specialists increased by 18.3% while drugs administered trended 10% less than market
  • High tech scans/1000 decreased by 12%
  • Overall performance index improved in Facility Outpatient (-5%), Other Medical Services (-6%) and Professional (-1%) relative to the market

An enviable performance considering the challenges the healthcare provider markets are facing with influx of changes in the market.

To lower health costs, physician networks and medical homes must employ a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in healthcare today.

To become masters in their population management programs, they need decision support systems such as population SOH (risk) stratification and predictive models. With the growth of EHR systems, claims-based risk analysis will replaced by clinically-driven models that leverage upto- the-minute clinical data to accurately determine state-of-health scores.

SOH scores are more accurate in relation to actual patient risk, and have extremely strong predictive power. Because they are based on actual clinical data rather than claims history, they are widely embraced by the physician community. Physicians actively look to SOH models to understand causes, predict outcomes, and focus on controllable factors to improve patient health. Since EHR, laboratory and pharmacy data is now widely available, SOH models are easy to build for most physician practices and medical homes. Ultimately, today’s physician has real power to bend the healthcare cost curve by focusing on high risk chronic patients, designing appropriate care management programs, and helping to keep patients out of hospitals.

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“The Vitreos Health State-of-Health scores really allowed us to embrace the Principles of Accountable Care Organization and transform ourselves with a data-driven decision making process…..Overall we were able to beat the market: For Outpatient Diagnostics, MCNT costs were up by 1.9% vs. Market up by 9.7%; and for Outpatient Surgeries costs, MCNT outpatient costs were up by 5.6% compared to the market at 15%.”

Karen Kennedy - CAO, MCNT from 1998 to 2013

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